import pandas as pd
import numpy as np
import os
import datetime
import matplotlib
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
from sklearn import tree
from sklearn import ensemble
import pytz
import itertools
import visualize
import utils
import pydotplus
import xgboost as xgb
from sklearn import metrics
from sklearn import model_selection
import pvlib
import cs_detection
import visualize_plotly as visualize
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
import plotly.graph_objs as go
import cufflinks as cf
cf.go_offline()
init_notebook_mode(connected=True)
from IPython.display import Image
%load_ext autoreload
%autoreload 2
np.set_printoptions(precision=4)
%matplotlib notebook
Only making ground predictions using PVLib clearsky model and statistical model. NSRDB model won't be available to ground measurements.
nsrdb = cs_detection.ClearskyDetection.read_pickle('abq_nsrdb_1.pkl.gz')
nsrdb.df.index = nsrdb.df.index.tz_convert('MST')
nsrdb.time_from_solar_noon('Clearsky GHI pvlib', 'tfn')
len(nsrdb.df)
train = cs_detection.ClearskyDetection(nsrdb.df, scale_col=None)
train.trim_dates(None, '01-01-2015')
test = cs_detection.ClearskyDetection(nsrdb.df, scale_col=None)
test.trim_dates('01-01-2015', None)
train.scale_model('GHI', 'Clearsky GHI pvlib', 'sky_status')
clf = ensemble.RandomForestClassifier(random_state=42)
utils.calc_all_window_metrics(train.df, 3, meas_col='GHI', model_col='Clearsky GHI pvlib', overwrite=True)
feature_cols = [
'tfn',
'abs_ideal_ratio_diff grad',
'abs_ideal_ratio_diff grad mean',
'abs_ideal_ratio_diff grad std',
'abs_ideal_ratio_diff grad second',
'abs_ideal_ratio_diff grad second mean',
'abs_ideal_ratio_diff grad second std',
'GHI Clearsky GHI pvlib line length ratio',
'GHI Clearsky GHI pvlib ratio',
'GHI Clearsky GHI pvlib ratio mean',
'GHI Clearsky GHI pvlib ratio std',
'GHI Clearsky GHI pvlib diff',
'GHI Clearsky GHI pvlib diff mean',
'GHI Clearsky GHI pvlib diff std'
]
target_cols = ['sky_status']
vis = visualize.Visualizer()
vis.plot_corr_matrix(train.df[feature_cols].corr(), feature_cols)
train = cs_detection.ClearskyDetection(nsrdb.df, scale_col=None)
train.trim_dates(None, '01-01-2015')
test = cs_detection.ClearskyDetection(nsrdb.df, scale_col=None)
test.trim_dates('01-01-2015', None)
utils.calc_all_window_metrics(train.df, 3, meas_col='GHI', model_col='Clearsky GHI pvlib', overwrite=True)
clf.fit(train.df[feature_cols].values, train.df[target_cols].values.flatten())
pred = test.iter_predict_daily(feature_cols, 'GHI', 'Clearsky GHI pvlib', clf, 3, multiproc=True, by_day=True).astype(bool)
metrics.accuracy_score(test.df['sky_status'], pred)
vis = visualize.Visualizer()
vis.add_line_ser(test.df['GHI'], 'GHI')
vis.add_line_ser(test.df['Clearsky GHI pvlib'], 'GHI_cs')
vis.add_circle_ser(test.df[(test.df['sky_status'] == 0) & (pred)]['GHI'], 'ML clear only')
vis.add_circle_ser(test.df[(test.df['sky_status'] == 1) & (~pred)]['GHI'], 'NSRDB clear only')
vis.add_circle_ser(test.df[(test.df['sky_status'] == 1) & (pred)]['GHI'], 'ML+NSRDB clear only')
vis.show()
cm = metrics.confusion_matrix(test.df['sky_status'].values, pred)
vis = visualize.Visualizer()
vis.plot_confusion_matrix(cm, labels=['cloudy', 'clear'])
bar = go.Bar(x=feature_cols, y=clf.feature_importances_)
iplot([bar])
import warnings
runs_df = pd.read_csv('8_abq_directional_features.csv')
runs_df[['accuracy', 'f1', 'recall', 'precision']].iplot(kind='box')
runs_df
train = cs_detection.ClearskyDetection(nsrdb.df)
train.trim_dates(None, '01-01-2015')
test = cs_detection.ClearskyDetection(nsrdb.df)
test.trim_dates('01-01-2015', None)
train.scale_model('GHI', 'Clearsky GHI pvlib', 'sky_status')
utils.calc_all_window_metrics(train.df, 3, meas_col='GHI', model_col='Clearsky GHI pvlib', overwrite=True)
best_recall = runs_df.iloc[runs_df['recall'].idxmax()]
params_recall = best_recall[['max_depth', 'n_estimators', 'min_samples_leaf']].to_dict()
params_recall
train = cs_detection.ClearskyDetection(nsrdb.df)
train.trim_dates(None, '01-01-2015')
test = cs_detection.ClearskyDetection(nsrdb.df)
test.trim_dates('01-01-2015', None)
train.scale_model('GHI', 'Clearsky GHI pvlib', 'sky_status')
utils.calc_all_window_metrics(train.df, 3, meas_col='GHI', model_col='Clearsky GHI pvlib', overwrite=True)
best_accuracy = runs_df.iloc[runs_df['accuracy'].idxmax()]
print(best_accuracy.equals(best_recall))
params_accuracy = best_accuracy[['max_depth', 'n_estimators', 'class_weight', 'min_samples_leaf']].to_dict()
params_accuracy
train = cs_detection.ClearskyDetection(nsrdb.df)
train.trim_dates(None, '01-01-2015')
test = cs_detection.ClearskyDetection(nsrdb.df)
test.trim_dates('01-01-2015', None)
train.scale_model('GHI', 'Clearsky GHI pvlib', 'sky_status')
utils.calc_all_window_metrics(train.df, 3, meas_col='GHI', model_col='Clearsky GHI pvlib', overwrite=True)
best_precision = runs_df.iloc[runs_df['precision'].idxmax()]
print(best_precision.equals(best_recall))
print(best_precision.equals(best_accuracy))
params_precision = best_precision[['max_depth', 'n_estimators', 'class_weight', 'min_samples_leaf']].to_dict()
params_precision
train = cs_detection.ClearskyDetection(nsrdb.df)
train.trim_dates(None, '01-01-2015')
test = cs_detection.ClearskyDetection(nsrdb.df)
test.trim_dates('01-01-2015', None)
train.scale_model('GHI', 'Clearsky GHI pvlib', 'sky_status')
utils.calc_all_window_metrics(train.df, 3, meas_col='GHI', model_col='Clearsky GHI pvlib', overwrite=True)
best_f1 = runs_df.iloc[runs_df['f1'].idxmax()]
print(best_f1.equals(best_recall))
print(best_f1.equals(best_accuracy))
print(best_f1.equals(best_precision))
best_f1
best_f1 = best_f1[['max_depth', 'min_samples_leaf', 'max_depth']].to_dict()
Same model as best recall - scroll up.
train = cs_detection.ClearskyDetection(nsrdb.df)
train.scale_model('GHI', 'Clearsky GHI pvlib', 'sky_status')
utils.calc_all_window_metrics(train.df, 3, meas_col='GHI', model_col='Clearsky GHI pvlib', overwrite=True)
clf = ensemble.RandomForestClassifier(**best_f1, n_estimators=100)
clf.fit(train.df[feature_cols].values, train.df[target_cols].values.flatten())
bar = go.Bar(x=feature_cols, y=clf.feature_importances_)
iplot([bar])
ground = cs_detection.ClearskyDetection.read_pickle('abq_ground_1.pkl.gz')
ground.df.index = ground.df.index.tz_convert('MST')
test = cs_detection.ClearskyDetection(ground.df)
test.trim_dates('10-01-2015', '11-01-2015')
test.df = test.df[test.df.index.minute % 30 == 0]
test.df.keys()
test.time_from_solar_noon('Clearsky GHI pvlib', 'tfn')
pred = test.iter_predict_daily(feature_cols, 'GHI', 'Clearsky GHI pvlib', clf, 3, multiproc=True, by_day=True).astype(bool)
train2 = cs_detection.ClearskyDetection(nsrdb.df)
train2.intersection(test.df.index)
nsrdb_clear = train2.df['sky_status'].values
ml_clear = pred
vis = visualize.Visualizer()
vis.add_line_ser(test.df['GHI'], 'GHI')
vis.add_line_ser(test.df['Clearsky GHI pvlib'], 'GHI_cs')
vis.add_circle_ser(test.df[ml_clear & ~nsrdb_clear]['GHI'], 'ML clear only')
vis.add_circle_ser(test.df[~ml_clear & nsrdb_clear]['GHI'], 'NSRDB clear only')
vis.add_circle_ser(test.df[ml_clear & nsrdb_clear]['GHI'], 'Both clear')
vis.show()
probas = clf.predict_proba(test.df[feature_cols].values)
test.df['probas'] = 0
test.df['probas'] = probas[:, 1]
visualize.plot_ts_slider_highligther(test.df, prob='probas')
In general, the clear sky identification looks good. At lower frequencies (30 min, 15 min) we see good agreement with NSRDB labeled points. I suspect this could be further improved my doing a larger hyperparameter search, or even doing some feature extraction/reduction/additions.